Association between lung function and the risk of atrial fibrillation in a nationwide population cohort study

We investigated the association between lung function and atrial fibrillation (AF) in 21,349 adults without AF aged ≥ 40 years who underwent spirometry. The study participants were enrolled from the Korean National Health and Nutritional Examination Survey between 2008 and 2016. The primary outcome was new-onset non-valvular AF identified from the National Health Insurance Service database. During the median follow-up of 6.5 years, 2.15% of participants developed new-onset AF. The incidence rate of AF per 1000 person-years was inversely related to the forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), and FEV1/FVC quartile. After adjustment for multiple variables, the AF risk in the lowest FEV1 quartile was 1.64-fold higher than that in the highest quartile (hazard ratio (HR) 1.64 (95% confidence interval (CI) 1.26–2.12) for lowest FEV1 quartile). The lowest quartile of FVC had 1.56-fold higher AF risk than the highest quartile (HR 1.56 (95% CI 1.18–2.08) for lowest FVC quartile). Although the lowest FEV1/FVC quartile was associated with an increased risk of AF in the unadjusted model, this increased risk was not statistically significant in the multivariable analysis. Compared to those with normal lung function, participants with restrictive or obstructive lung function had 1.49 and 1.42-fold higher AF risks, respectively. In this large nationwide cohort study, both obstructive and restrictive patterns of reduced lung function were significantly associated with increased AF risk.

Lung function quartile and AF. The incidence of AF was inversely related to FEV 1 , FVC, and FEV 1 /FVC quartiles. From the highest to lowest quartiles, the incidence rates were 2.82, 2.55, 3.10, and 5.08 for FEV 1 ; 2.11, 2.64, 3.52, and 5.32 for FVC; 1.87, 2.60, 3.51, and 5.71 for FEV 1 /FVC (Table 3). Therefore, lung function quartiles were inversely associated with AF risk. After adjustment for multiple variables, the AF risks were 1.64-fold higher for the lowest compared to the highest FEV 1 quartile (HR 1.64 (95% CI 1.26-2.12) for the lowest FEV 1 quartile), and 1.56-fold higher for the lowest compared to the highest FVC quartile (HR 1.56 (95% CI 1.18-2.08) for the lowest FVC quartile), respectively. Although the lowest FEV 1 /FVC quartile was associated with increased AF risk in the unadjusted model, this inverse association was not statistically significant in the multivariable analysis. Figure 1 shows the AF incidence rates and HRs by lung function deciles.
Subgroup analysis. Figure 3 shows the results of the subgroup analysis of reduced lung function and AF risk according to age, sex, smoking, alcohol consumption, DM, hypertension, CKD, stroke, and IHD. The association between reduced lung function and AF risk did not vary according to any of these factors. There were no significant interactions observed in any subgroup.

Discussion
In this large national cohort study, we found that reduced lung function was associated with increased AF risk, with similar risks for obstructive and restrictive lung function impairments. Lung function quartiles were inversely related to the AF risk. The main finding of the present study was that individuals with obstructive or restrictive lung function impairments had approximately 1.4-fold higher AF risk than those with normal lung function.
Most studies of pulmonary function and AF risk have been multicenter cohort studies that mainly focused on the FEV 1 and FVC values, rather than categories of lung function impairment [11][12][13][14] . Moreover, previous studies had significant differences in the FEV 1 and FVC categories, follow-up duration and study design. A large Swedish population-based cohort study with a long follow-up duration of 24.8 years showed an inverse relationship between lung function and AF incidence by gender, but many patients were treated in an outpatient rather than an inpatient setting, hospitalized patients were excluded from the study 10 . The Atherosclerosis Risk www.nature.com/scientificreports/ In Communities (ARIC) study also reported an inverse relationship between lung function and AF, with a relatively high rate of AF development (11%) during the follow-up of 11 years. However, in the ARIC study, 73.8% of participants were White, who have a higher AF risk than Asians 12,13 . In addition, the ARIC investigators reported that obstructive lung function impairment was associated with increased AF incidence. However, they did not analyze restrictive lung function impairment in the study. In the Multi-Ethnic Study of Atherosclerosis (MESA) study, the AF incidence varied by race and was significantly lower in non-White patients compared to White patients 15 . In a cohort study conducted in specific regions of Korea, new-onset AF was identified in 1.7% www.nature.com/scientificreports/ of the population, and individuals in the lowest FEV 1 quartile had 1.59-fold higher AF risk than those in the highest quartile, which is in line with the results of our study. However, in the previous study, AF was not defined using the ICD-10 code 14 . In the present nationwide population cohort study, including data from the baseline physical examination, laboratory findings, outpatient visits, hospitalization, and medication claims, the AF risk was significantly inversely associated with FEV 1 and FVC quartiles after multivariable adjustment. Interestingly, patients with obstructive or restrictive lung function impairment had a 1.4-fold higher AF incidence than those with normal lung function.  www.nature.com/scientificreports/ The mechanisms underlying the association between decreased lung function and AF are unclear. However, previous studies have suggested several mechanisms, including hypoxia, systemic inflammation, increased sympathetic nerve activation, and the use of COPD treatments, such as beta-2 agonists and oral steroids 6 . Hypoxia is common in individuals with decreased lung function and causes pulmonary vasoconstriction, which leads to pulmonary hypertension and increased afterload to the right side of the heart 16,17 . Moreover, chronic hypoxia regulates the expression of hypoxia-inducible factors 1 and 2, the production of reactive oxygen species, blood pressure, and vascular inflammation 18 . Atrial structural remodeling plays a key role in the development of AF in patients with impaired lung function. In addition, elevated white blood cell count and C-reactive protein levels are associated with increased AF incidence 19 . Fogarty et al. 20 reported that the C-reactive protein level is inversely related to lung function. The present study also showed that the white blood cell count was elevated in individuals with restrictive and obstructive lung function impairments, in line with previous studies. Sympathetic nerve activation, which is related to AF progression, is often found in patients with reduced lung function 21 . Inhaled beta-2 agonists and anticholinergics, the main treatments for COPD, are highly associated with tachyarrhythmia 22 . In addition, oral corticosteroids and theophylline increase the risk of AF 23 .
The present study of nationwide population showed increased AF incidence in patients with reduced lung function. Therefore, individuals with reduced lung function may be potential candidates for more meticulous screening of AF. To the best of our knowledge, this was the first study to investigate the association between obstructive or restrictive lung function impairment and AF risk using detailed anthropometric and laboratory results in a nationwide cohort.
This study has several limitations. First, clinical outcomes relied only on claims data and there might be missed diagnoses of asymptomatic and paroxysmal AF. Second, the pulmonary function tests used in this study could not assess the severity of COPD because of the pre-bronchodilator results. Third, this study was conducted with a single Asian ethnic group. Fourth, we measured lung function using only one method, spirometry without chest computed tomography, which indicates the presence or absence of bronchiectasis or emphysema. However, spirometry is a simple and easy method to check lung function, and trained examiners measured lung function with a predefined protocol. Fifth, medications that could increase the incidence of AF, such as beta-2 agonists and oral steroids were not considered. Sixth, compared to population without lung function impairment, those with obstructive or restrictive lung function impairment may have higher detection rate of AF, due to this group tends to get more medical attention and examination. Last, in this study, more subjects had minor reduced lung function than severely reduced lung function, because KNHANES data were obtained from subjects participating in a national survey. However, the KNHANES data can represent the public health of the entire population of Korea, which has significant implications in many respects 24 . www.nature.com/scientificreports/

Conclusions
We found that reduced lung function was an independent risk factor for AF in a nationwide population cohort study. Subjects with impaired lung function have a high risk of AF, and both obstructive and restrictive lung function impairment have similar AF risks. Future prospective research is needed to clarify the mechanism of association between reduced lung function and AF.

Methods
Source of the database and study population. We derived the data by cross-referencing KNHANES and NHIS. In this study, we used KNHANES data to collect study population and NHIS data to determine clinical outcomes. The KNHANES has been performed by the Korean Centers for Disease Control and Prevention at 3-year intervals since 1998 to monitor the general health and nutritional status of the civilian, noninstitutionalized Korean population 25 . Sampling units were households selected via a stratified, multistage probabilitysampling design that considered geographic area, sex, and age group, by reference to household registries. NHIS is a social insurance payment system that covers approximately 97% of the Korean population. The Korean NHIS data include all national health checkup data and claim data, including drug prescriptions, diagnostic codes for the International Classification of Disease-10 (ICD-10) disease coding system, and claimed treatment details 26 . All KNHANES participants provided written informed consent for participation. In this study, data from the KNHANES between 2008 and 2016 were used. We excluded participants among the 40,279 sample individuals as follow, aged < 40 years (n = 4772), participants without spirometry results (n = 10,705), participants who were already diagnosed with AF during wash-out period for 1 year (n = 458), or missing data (n = 2995) (Fig. 4).
The institutional review board of the Catholic University of Korea (IRB no: VC21ZISI0041) approved this study. The study was conducted in compliance with the Declaration of Helsinki. Informed consent was obtained from all participants at the time of survey collection.
Clinical and laboratory measurements. Details of the KNHANES regarding health surveys, standardized physical examinations, laboratory tests, and definitions of risk factors have been described in a previous www.nature.com/scientificreports/ paper 25 . Specially trained examiners performed the physical examination by a standardized method. Body mass index (BMI) was calculated as participant body weight in kilograms divided by the square of height in meters. Obesity was defined as subjects with BMI more than 25 kg/m 227 . Waist circumference (WC) was measured at the midpoint between the lowest rib and the anterior iliac crest in the standing position. Abdominal obesity was defined as subjects with WC more than 90 cm in men and more than 80 cm in women. The definition of abdominal obesity was a WC ≥ 90 cm in men and ≥ 85 cm 28 . However, our data were provided by masking in 10 cm increments, and the standard of 80 cm in women has been applied. The health-related behavior surveys included well-established questionnaires to determine the demographic and socioeconomic characteristics of the population. Smoking status was divided into three categories: nonsmoker, ex-smoker, or current smoker. Alcohol consumption was assessed based on the average number of alcoholic beverages and frequency of drinking. Heavy drinkers were defined as subjects who drank more than 30 g/day, and subjects drinking less than 30 g/day were classified as mild to moderate drinkers 29 . Moderate physical activity was defined as walking at least 150 min per week 29 . Household income was divided into quartile groups: lowest, lower middle, higher middle, and highest. Occupation status was divided into yes or no. High education level was defined as subjects who finished high school education or more. DM was defined as a fasting glucose level ≥ 126 mg/dL, current use of antidiabetic medications, or a selfreported physician diagnosis of DM 30 . Hypertension was defined as systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg, the current use of antihypertensive medications, or a self-reported physician diagnosis of hypertension 31 . Dyslipidemia was defined as total cholesterol ≥ 240 mg/dL, use of cholesterollowering medications, or a self-reported physician diagnosis of dyslipidemia. Asthma, prior stroke, and prior IHD were defined as a self-reported diagnosis by a physician in the health interview surveys 31 .
After overnight fasting, blood samples were collected from participants' antecubital veins. The estimated glomerular filtration rate (eGFR) was calculated using the Modification of Renal Diet equation from baseline serum creatinine 32 . CKD was defined as eGFR < 60 mL/min/1.73 m 2 for 3 months or more.  Clinical outcomes. The primary outcome was newly diagnosed non-valvular AF, either one diagnosis (ICD-10 code of I48.0-I48.4, I48.9) during hospitalization or more than two diagnoses in the outpatient clinics, until censoring or death 38 . A washout period of 1 year was set to ensure AF was newly diagnosed. To evaluate newly diagnosed AF, we used data derived from a 2007 cohort of the NHIS with clinical follow-up for the primary outcome through December 31, 2018.
Statistical analysis. Summary statistics are expressed as the means and standard deviations for continuous variables and as numbers and percentages for categorical variables. Continuous variables were compared using Student's t-test or analysis of variance, as appropriate. Categorical variables were compared using the chisquare test. Multiple comparisons were assessed using the Bonferonni correction. The IR of AF was calculated by dividing the number of AF cases by the sum of the follow-up duration and is presented as the rate per 1000 person-years. Participants were followed until the first diagnosis of AF, censoring by death, or December 31, 2018. Clinical outcomes were determined using the Kaplan-Meier method and compared using the log-rank test. Cox-proportional-hazard models were performed to analyze the impact of lung function on AF risk. The hazard ratio (HR) and 95% confidence interval (CI) were also calculated. A p value < 0.05 was considered statistically significant. To account for potentially confounding clinical covariates and to adjust the established risk factors for AF, multivariable Cox regression models were adjusted for age and sex (model 1); age, sex, BMI, household income, education, exercise, alcohol consumption, smoking, hypertension, DM, and dyslipidemia (model 2); and the variables in model 2 plus white blood cell count, eGFR, stroke, IHD, and asthma (model 3). Time-scale of the models was time on study. Statisticians confirmed the proportional hazard assumption through statistical tests based on Schoenfeld residuals and graphical diagnosis through log-log plots. In Fig. 1, p for trend was calculated through cox proportional regression analysis by considering the decile as a continuous variable. We also conducted subgroup analyses stratified by the confounding factors for the sensitivity analysis. The potential effect of modifications by the subgroups was evaluated using stratified analysis and interaction testing with a likelihood ratio test. All statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC, USA).